The development of nuclear technologies has directed environmental radioactivity research
toward continuously improving existing and develo** new models for different …

Abstract Machine learning algorithms have progressively become a part of landslide
susceptibility map** practices owing to their robustness in dealing with complicated and …

During nuclear accidents, large amounts of short-lived radionuclides are released into the
environment, causing acute health hazards to local populations. Therefore, it is particularly …

Modern society is confronted with emerging threats from chemical, biological, and
radiological (CBR) hazardous substances, which are intensively utilized in the chemical …

During severe nuclear accidents, radioactive materials are expected to be released into the
atmosphere. Estimating the source term plays a significant role in assessing the …

It is extremely critical for an emergency response to quickly and accurately use source term
estimation (STE) in the event of hazardous gas leakage. To determine the appropriate …

In this study, we rigorously assess the performance of three gradient-free optimization
algorithms—Ensemble Kalman Inversion (EKI), Particle Swarm Optimization (PSO), and …

Release rate estimation is a vital means of revealing the emission process of radionuclides
and assessing the environmental consequences in an emergency. Inverse modeling is …

When a nuclear accident occurs, a large number of radioactive nuclides are released into
the environment, seriously affecting the environment and human health. Machine learning …

Source term estimation (STE) of hazardous gas leakages in chemical industrial parks (CIPs)
is important for addressing environmental pollution and improving engineering safety and …